Wireless Sensor Networks (WSNs) are composed of energy-constrained sensor nodes deployed in a geographic area for monitoring and data collection purposes. Clustering algorithms significantly improve network performance by reducing the number of transmissions, optimizing energy consumption, and enhancing overall scalability, making them an effective solution for resource-constrained networks. Our algorithm assumes all nodes are localized using signal intensity from neighbors. Each node creates a global clustering map through a three-pass process. The first pass estimates the desired clusters using node count and average neighbors. The second pass employs iterative clustering with nearest-node grouping and furthest-node cluster head selection. The third pass optimizes clustering by regrouping based on paths between cluster heads and member nodes. The dynamic cluster head selection process in the suggested method takes into account variables including connectedness with other nodes, communication range, and residual energy. The proposed algorithm outperforms K-means, achieving a mean total distance reduction of approximately 90.50%, which translates into an energy savings of around 99.10%. This improvement underscores the profound impact of distance reduction on energy efficiency, leveraging the quadratic relationship between energy consumption and transmission distance. By optimizing communication paths, the algorithm minimizes unnecessary transmissions, adapts seamlessly to varying scenarios, and extends network lifetime, making it an attractive solution for energy-constrained WSNs. This paper introduces a fast, efficient three-pass clustering algorithm for WSNs and a custom Python-based GUI simulator. The open-source code is shared to support fellow researchers.